Simulation optimization with qualitative variables and structural model changes: A genetic algorithm approach

In many common simulation optimization methods the structure of the system stays the same and only the set of values for certain parameters of the system such as the number of machines in a station or the in-process inventory is varied from one evaluation to the next. The methodology described in this paper is a simulation-optimization process where the qualitative variables and the structure of the system are the subjects of optimization. Here, the optimum response sought is a function of design and operation characteristics of the system such as the type of machines to use, dispatching rules, sequence of processing operations, etc. In the methodology developed here simulation models are automatically generated through an object-oriented process and are evaluated for various candidate configurations of the system. These candidates are suggested by a Genetic Algorithm (GA) that automatically guides the system towards better solutions. After simulating the alternatives, the results are returned to the GA to be utilized in selection of the next generation of configurations to be evaluated. This process continues until a satisfactory solution is obtained for the system.